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  2. Document retrieval - Wikipedia

    en.wikipedia.org/wiki/Document_retrieval

    Most content based document retrieval systems use an inverted index algorithm. A signature file is a technique that creates a quick and dirty filter, for example a Bloom filter, that will keep all the documents that match to the query and hopefully a few ones that do not. The way this is done is by creating for each file a signature, typically ...

  3. Document-term matrix - Wikipedia

    en.wikipedia.org/wiki/Document-term_matrix

    Shortly thereafter, Gerard Salton published "Some hierarchical models for automatic document retrieval" in 1963 which also included a visual depiction of a document-term matrix. [5] Salton was at Harvard University at the time and his work was supported by the Air Force Cambridge Research Laboratories and Sylvania Electric Products, Inc.

  4. Search engine indexing - Wikipedia

    en.wikipedia.org/wiki/Search_engine_indexing

    Stores citations or hyperlinks between documents to support citation analysis, a subject of bibliometrics. n-gram index Stores sequences of length of data to support other types of retrieval or text mining. [13] Document-term matrix Used in latent semantic analysis, stores the occurrences of words in documents in a two-dimensional sparse matrix.

  5. Information retrieval - Wikipedia

    en.wikipedia.org/wiki/Information_retrieval

    1983: Salton (and Michael J. McGill) published Introduction to Modern Information Retrieval (McGraw-Hill), with heavy emphasis on vector models. 1985: David Blair and Bill Maron publish: An Evaluation of Retrieval Effectiveness for a Full-Text Document-Retrieval System mid-1980s: Efforts to develop end-user versions of commercial IR systems.

  6. Okapi BM25 - Wikipedia

    en.wikipedia.org/wiki/Okapi_BM25

    The fuller name, Okapi BM25, includes the name of the first system to use it, which was the Okapi information retrieval system, implemented at London's City University [1] in the 1980s and 1990s. BM25 and its newer variants, e.g. BM25F (a version of BM25 that can take document structure and anchor text into account), represent TF-IDF -like ...

  7. Vector space model - Wikipedia

    en.wikipedia.org/wiki/Vector_space_model

    Candidate documents from the corpus can be retrieved and ranked using a variety of methods. Relevance rankings of documents in a keyword search can be calculated, using the assumptions of document similarities theory, by comparing the deviation of angles between each document vector and the original query vector where the query is represented as a vector with same dimension as the vectors that ...

  8. XML retrieval - Wikipedia

    en.wikipedia.org/wiki/XML_Retrieval

    Ranking in XML-Retrieval can incorporate both content relevance and structural similarity, which is the resemblance between the structure given in the query and the structure of the document. Also, the retrieval units resulting from an XML query may not always be entire documents, but can be any deeply nested XML elements, i.e. dynamic documents.

  9. Term-document matrix - Wikipedia

    en.wikipedia.org/?title=Term-document_matrix&...

    Download as PDF; Printable version; In other projects Appearance. move to sidebar hide. From Wikipedia, the free encyclopedia. Redirect page. Redirect to: Document ...